ESTIMULANTES EN LA GERMINACIÓN Y BIOMETRÍA INICIAL DE DOS VARIEDADES DE MAÍZ MORADO (Zea mays L.)

1 Setup

Instalar version en desarrollo.

if (!require("remotes"))
  install.packages("remotes")
remotes::install_github("flavjack/inti")

library(emmeans)
library(corrplot)
library(multcomp)
source('https://inkaverse.com/setup.r')

cat("Project: ", getwd())

Project:  C:/Users/floza/git/prochira_maiz_morado

session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Latin America.utf8
 ctype    Spanish_Latin America.utf8
 tz       America/Lima
 date     2024-07-29
 pandoc   3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version  date (UTC) lib source
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 hms             1.1.3    2023-03-21 [1] CRAN (R 4.4.0)
 htmltools       0.5.8.1  2024-04-04 [1] CRAN (R 4.4.0)
 htmlwidgets     1.6.4    2023-12-06 [1] CRAN (R 4.4.0)
 httpuv          1.6.15   2024-03-26 [1] CRAN (R 4.4.0)
 httr            1.4.7    2023-08-15 [1] CRAN (R 4.4.0)
 huito         * 0.2.4    2023-10-25 [1] CRAN (R 4.4.0)
 inti          * 0.6.5    2024-07-29 [1] Github (flavjack/inti@38be898)
 jsonlite        1.8.8    2023-12-04 [1] CRAN (R 4.4.0)
 knitr         * 1.48     2024-07-07 [1] CRAN (R 4.4.1)
 later           1.3.2    2023-12-06 [1] CRAN (R 4.4.0)
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 lme4            1.1-35.5 2024-07-03 [1] CRAN (R 4.4.1)
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 MASS          * 7.3-60.2 2024-04-26 [2] CRAN (R 4.4.1)
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 mime            0.12     2021-09-28 [1] CRAN (R 4.4.0)
 miniUI          0.1.1.1  2018-05-18 [1] CRAN (R 4.4.0)
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 mnormt          2.1.1    2022-09-26 [1] CRAN (R 4.4.0)
 multcomp      * 1.4-26   2024-07-18 [1] CRAN (R 4.4.1)
 multcompView    0.1-10   2024-03-08 [1] CRAN (R 4.4.0)
 munsell         0.5.1    2024-04-01 [1] CRAN (R 4.4.0)
 mvtnorm       * 1.2-5    2024-05-21 [1] CRAN (R 4.4.0)
 nlme            3.1-164  2023-11-27 [2] CRAN (R 4.4.1)
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 openssl         2.2.0    2024-05-16 [1] CRAN (R 4.4.0)
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 pkgconfig       2.0.3    2019-09-22 [1] CRAN (R 4.4.0)
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 xtable          1.8-4    2019-04-21 [1] CRAN (R 4.4.0)
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 [1] C:/Users/floza/AppData/Local/R/win-library/4.4
 [2] C:/Program Files/R/R-4.4.1/library

──────────────────────────────────────────────────────────────────────────────

2 Refrencias

• (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
• (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
• (HCPC) https://youtu.be/EJqYTDTJJug

3 Import data

https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741

url <- "https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741"

gs <- url %>% 
  as_sheets_id()

imbibition <- gs %>% 
  range_read("imbibition") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(time = tiempo, .after = tiempo) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("Hibrido") ~ "Hybrid"
  )) %>% 
  mutate(across(1:tiempo, ~ as.factor(.))) 

str(imbibition)
## tibble [2,100 × 7] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
##  $ trat       : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad   : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ tiempo     : Factor w/ 5 levels "0","3","6","9",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ time       : num [1:2100] 0 0 0 0 0 0 0 0 0 0 ...
##  $ peso       : num [1:2100] 0.58 0.62 0.73 0.72 0.72 0.68 0.71 0.61 0.69 0.64 ...

germination <- gs %>% 
  range_read("germination") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("Hibrido") ~ "Hybrid"
  )) %>% 
  mutate(trat = case_when(
    tratamiento %in% "Agua Destilada" ~ "T0"
    , tratamiento %in% "Algas Marinas 1 L/cil" ~ "T1"
    , tratamiento %in% "Algas Marinas 1,5 L/cil" ~ "T2"
    , tratamiento %in% "Azufre 100 gr.200 L-1" ~ "T3"
    , tratamiento %in% "Azufre 150 gr.200 L-1" ~ "T4"
    , tratamiento %in% "Suero de leche 10%" ~ "T5"
    , tratamiento %in% "Suero de leche 30%" ~ "T6"
  ), .before = tratamiento) %>% 
  mutate(across(1:variedad, ~ as.factor(.))) 

str(germination)
## tibble [42 × 11] (S3: tbl_df/tbl/data.frame)
##  $ bloque     : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
##  $ trat       : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 2 2 2 3 3 3 4 ...
##  $ variedad   : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dia 1      : num [1:42] 2 4 3 0 1 1 0 0 1 0 ...
##  $ dia 2      : num [1:42] 5 4 5 3 2 1 1 4 5 1 ...
##  $ dia 3      : num [1:42] 1 1 1 1 0 0 0 0 0 0 ...
##  $ total      : num [1:42] 8 9 9 4 3 2 1 4 6 1 ...
##  $ pg         : num [1:42] 80 90 90 40 30 20 10 40 60 10 ...
##  $ vg         : num [1:42] 2.67 3 3 2 1.5 ...
##  $ ig         : num [1:42] 2.4 2.7 2.7 0.8 0.6 0.4 0.1 0.4 1.2 0.1 ...

plantula <- gs %>% 
  range_read("plantula") %>% 
  rename_with(~ tolower(.)) %>% 
  mutate(variedad = case_when(
    variedad %in% c("criollo") ~ "Creole"
    , variedad %in% c("hibrido") ~ "Hybrid"
  )) %>% 
  mutate(across(1:variedad, ~ as.factor(.)))

str(plantula)
## tibble [210 × 16] (S3: tbl_df/tbl/data.frame)
##  $ trat           : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ tratamiento    : Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ variedad       : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
##  $ raiz_lgtd      : num [1:210] 11 8 12 11 8 13 10 12 9 13 ...
##  $ gsr_raiz       : num [1:210] 1.3 1.19 1.51 1.21 1.17 1.13 1.68 1.27 1.03 1.16 ...
##  $ num_raiz       : num [1:210] 8 11 11 9 12 16 10 9 16 11 ...
##  $ peso_fres_raiz : num [1:210] 4.82 3.21 4.91 4.42 4.62 6.07 4.97 6.13 3.05 4 ...
##  $ peso_seco_raiz : num [1:210] 0.73 0.41 0.62 0.66 0.72 0.54 0.75 0.56 0.57 0.74 ...
##  $ alt_planta     : num [1:210] 30 26 28 32 25 27 28 35 29 29 ...
##  $ gsr_tallo      : num [1:210] 5.86 4.56 6.59 4.63 4.55 4.14 4.02 4.32 3.45 3.61 ...
##  $ nhp_hoja       : num [1:210] 5 5 5 6 4 5 5 5 5 5 ...
##  $ larg_hoja      : num [1:210] 26 23 21 27 29 22 24 30 25 23 ...
##  $ grs_hoja       : num [1:210] 0.94 1.15 0.89 0.98 1.01 0.72 0.62 1.03 0.71 1.34 ...
##  $ anch_hoja      : num [1:210] 19.3 19.9 21.5 17.3 18.9 ...
##  $ peso_fres_brote: num [1:210] 5.34 5.99 5.45 4.81 7.03 6.79 4.99 4.53 3.56 4 ...
##  $ peso_seco_brote: num [1:210] 0.5 0.49 1.04 0.78 0.68 0.67 0.69 0.78 0.73 0.75 ...

4 Tratamientos

imbibition %>% 
  group_by(trat, tratamiento) %>% 
  summarise(n = n()) %>% 
  select(!n)
## # A tibble: 7 × 2
## # Groups:   trat [7]
##   trat  tratamiento            
##   <fct> <fct>                  
## 1 T0    Agua Destilada         
## 2 T1    Algas Marinas 1 L/cil  
## 3 T2    Algas Marinas 1,5 L/cil
## 4 T3    Azufre 100 gr.200 L-1  
## 5 T4    Azufre 150 gr.200 L-1  
## 6 T5    Suero de leche 10%     
## 7 T6    Suero de leche 30%

5 Data summary

sm <- imbibition %>% 
  group_by(tratamiento, variedad, tiempo) %>% 
  summarise(across(peso, ~ sum(!is.na(.))))

sm
## # A tibble: 70 × 4
## # Groups:   tratamiento, variedad [14]
##    tratamiento    variedad tiempo  peso
##    <fct>          <fct>    <fct>  <int>
##  1 Agua Destilada Creole   0         30
##  2 Agua Destilada Creole   3         30
##  3 Agua Destilada Creole   6         30
##  4 Agua Destilada Creole   9         30
##  5 Agua Destilada Creole   12        30
##  6 Agua Destilada Hybrid   0         30
##  7 Agua Destilada Hybrid   3         30
##  8 Agua Destilada Hybrid   6         30
##  9 Agua Destilada Hybrid   9         30
## 10 Agua Destilada Hybrid   12        30
## # ℹ 60 more rows

sm <- germination %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(pg:ig, ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 5
## # Groups:   tratamiento [7]
##    tratamiento             variedad    pg    vg    ig
##    <fct>                   <fct>    <int> <int> <int>
##  1 Agua Destilada          Creole       3     3     3
##  2 Agua Destilada          Hybrid       3     3     3
##  3 Algas Marinas 1 L/cil   Creole       3     3     3
##  4 Algas Marinas 1 L/cil   Hybrid       3     3     3
##  5 Algas Marinas 1,5 L/cil Creole       3     3     3
##  6 Algas Marinas 1,5 L/cil Hybrid       3     3     3
##  7 Azufre 100 gr.200 L-1   Creole       3     3     3
##  8 Azufre 100 gr.200 L-1   Hybrid       3     3     3
##  9 Azufre 150 gr.200 L-1   Creole       3     3     3
## 10 Azufre 150 gr.200 L-1   Hybrid       3     3     3
## 11 Suero de leche 10%      Creole       3     3     3
## 12 Suero de leche 10%      Hybrid       3     3     3
## 13 Suero de leche 30%      Creole       3     3     3
## 14 Suero de leche 30%      Hybrid       3     3     3

sm <- plantula %>% 
  group_by(tratamiento, variedad) %>% 
  summarise(across(where(is.numeric), ~ sum(!is.na(.))))

sm
## # A tibble: 14 × 15
## # Groups:   tratamiento [7]
##    tratamiento             variedad raiz_lgtd gsr_raiz num_raiz peso_fres_raiz
##    <fct>                   <fct>        <int>    <int>    <int>          <int>
##  1 Agua Destilada          Creole          15       15       15             15
##  2 Agua Destilada          Hybrid          15       15       15             15
##  3 Algas Marinas 1 L/cil   Creole          15       15       15             15
##  4 Algas Marinas 1 L/cil   Hybrid          15       15       15             15
##  5 Algas Marinas 1,5 L/cil Creole          15       15       15             15
##  6 Algas Marinas 1,5 L/cil Hybrid          15       15       15             15
##  7 Azufre 100 gr.200 L-1   Creole          15       15       15             15
##  8 Azufre 100 gr.200 L-1   Hybrid          15       15       15             15
##  9 Azufre 150 gr.200 L-1   Creole          15       15       15             15
## 10 Azufre 150 gr.200 L-1   Hybrid          15       15       15             15
## 11 Suero de leche 10%      Creole          15       15       15             15
## 12 Suero de leche 10%      Hybrid          15       15       15             15
## 13 Suero de leche 30%      Creole          15       15       15             15
## 14 Suero de leche 30%      Hybrid          15       15       15             15
## # ℹ 9 more variables: peso_seco_raiz <int>, alt_planta <int>, gsr_tallo <int>,
## #   nhp_hoja <int>, larg_hoja <int>, grs_hoja <int>, anch_hoja <int>,
## #   peso_fres_brote <int>, peso_seco_brote <int>

6 Objetivos

1. Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

2. Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.

6.1 Objetivo Específico 1

Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.

• Imbibiciación, % germinación, velocidad e IG

6.1.1 Imbibición

trait <- "peso"
fb <- imbibition

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad +  (1 + tiempo|tratamiento)") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + tiempo +  trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers %>% kable()

index	bloque	trat	variedad	tiempo	tratamiento	peso	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: peso
##                 Df  Sum Sq Mean Sq  F value              Pr(>F)    
## bloque           2  0.0021 0.00105   0.1222               0.885    
## tiempo           4 10.0058 2.50146 289.7715 <0.0000000000000002 ***
## trat             6  3.2174 0.53624  62.1186 <0.0000000000000002 ***
## variedad         1  0.6165 0.61649  71.4150 <0.0000000000000002 ***
## trat:variedad    6  2.6467 0.44111  51.0987 <0.0000000000000002 ***
## Residuals     2080 17.9556 0.00863                                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ tiempo|variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	tiempo	variedad	trat	emmean	SE	df	lower.CL	upper.CL	group
1	12	Creole	T0	0.8296986	0.0086019	2080	0.8128293	0.8465678	a
3	9	Creole	T0	0.8279129	0.0086019	2080	0.8110436	0.8447821	a
2	3	Creole	T0	0.7645081	0.0086019	2080	0.7476388	0.7813774	b
4	6	Creole	T0	0.7620295	0.0086019	2080	0.7451603	0.7788988	b
5	0	Creole	T0	0.6398176	0.0086019	2080	0.6229483	0.6566869	c
11	12	Creole	T1	0.7157719	0.0086019	2080	0.6989026	0.7326412	a
13	9	Creole	T1	0.7139862	0.0086019	2080	0.6971169	0.7308555	a
12	3	Creole	T1	0.6505814	0.0086019	2080	0.6337122	0.6674507	b
14	6	Creole	T1	0.6481029	0.0086019	2080	0.6312336	0.6649721	b
15	0	Creole	T1	0.5258910	0.0086019	2080	0.5090217	0.5427602	c
21	12	Creole	T2	0.6749719	0.0086019	2080	0.6581026	0.6918412	a
23	9	Creole	T2	0.6731862	0.0086019	2080	0.6563169	0.6900555	a
22	3	Creole	T2	0.6097814	0.0086019	2080	0.5929122	0.6266507	b
24	6	Creole	T2	0.6073029	0.0086019	2080	0.5904336	0.6241721	b
25	0	Creole	T2	0.4850910	0.0086019	2080	0.4682217	0.5019602	c
31	12	Creole	T3	0.6591052	0.0086019	2080	0.6422360	0.6759745	a
33	9	Creole	T3	0.6573195	0.0086019	2080	0.6404503	0.6741888	a
32	3	Creole	T3	0.5939148	0.0086019	2080	0.5770455	0.6107840	b
34	6	Creole	T3	0.5914362	0.0086019	2080	0.5745669	0.6083055	b
35	0	Creole	T3	0.4692243	0.0086019	2080	0.4523550	0.4860936	c
41	12	Creole	T4	0.6322386	0.0086019	2080	0.6153693	0.6491078	a
43	9	Creole	T4	0.6304529	0.0086019	2080	0.6135836	0.6473221	a
42	3	Creole	T4	0.5670481	0.0086019	2080	0.5501788	0.5839174	b
44	6	Creole	T4	0.5645695	0.0086019	2080	0.5477003	0.5814388	b
45	0	Creole	T4	0.4423576	0.0086019	2080	0.4254883	0.4592269	c
51	12	Creole	T5	0.8092386	0.0086019	2080	0.7923693	0.8261078	a
53	9	Creole	T5	0.8074529	0.0086019	2080	0.7905836	0.8243221	a
52	3	Creole	T5	0.7440481	0.0086019	2080	0.7271788	0.7609174	b
54	6	Creole	T5	0.7415695	0.0086019	2080	0.7247003	0.7584388	b
55	0	Creole	T5	0.6193576	0.0086019	2080	0.6024883	0.6362269	c
61	12	Creole	T6	0.7740386	0.0086019	2080	0.7571693	0.7909078	a
63	9	Creole	T6	0.7722529	0.0086019	2080	0.7553836	0.7891221	a
62	3	Creole	T6	0.7088481	0.0086019	2080	0.6919788	0.7257174	b
64	6	Creole	T6	0.7063695	0.0086019	2080	0.6895003	0.7232388	b
65	0	Creole	T6	0.5841576	0.0086019	2080	0.5672883	0.6010269	c
6	12	Hybrid	T0	0.7764386	0.0086019	2080	0.7595693	0.7933078	a
8	9	Hybrid	T0	0.7746529	0.0086019	2080	0.7577836	0.7915221	a
7	3	Hybrid	T0	0.7112481	0.0086019	2080	0.6943788	0.7281174	b
9	6	Hybrid	T0	0.7087695	0.0086019	2080	0.6919003	0.7256388	b
10	0	Hybrid	T0	0.5865576	0.0086019	2080	0.5696883	0.6034269	c
16	12	Hybrid	T1	0.7279719	0.0086019	2080	0.7111026	0.7448412	a
18	9	Hybrid	T1	0.7261862	0.0086019	2080	0.7093169	0.7430555	a
17	3	Hybrid	T1	0.6627814	0.0086019	2080	0.6459122	0.6796507	b
19	6	Hybrid	T1	0.6603029	0.0086019	2080	0.6434336	0.6771721	b
20	0	Hybrid	T1	0.5380910	0.0086019	2080	0.5212217	0.5549602	c
26	12	Hybrid	T2	0.7881052	0.0086019	2080	0.7712360	0.8049745	a
28	9	Hybrid	T2	0.7863195	0.0086019	2080	0.7694503	0.8031888	a
27	3	Hybrid	T2	0.7229148	0.0086019	2080	0.7060455	0.7397840	b
29	6	Hybrid	T2	0.7204362	0.0086019	2080	0.7035669	0.7373055	b
30	0	Hybrid	T2	0.5982243	0.0086019	2080	0.5813550	0.6150936	c
36	12	Hybrid	T3	0.7332386	0.0086019	2080	0.7163693	0.7501078	a
38	9	Hybrid	T3	0.7314529	0.0086019	2080	0.7145836	0.7483221	a
37	3	Hybrid	T3	0.6680481	0.0086019	2080	0.6511788	0.6849174	b
39	6	Hybrid	T3	0.6655695	0.0086019	2080	0.6487003	0.6824388	b
40	0	Hybrid	T3	0.5433576	0.0086019	2080	0.5264883	0.5602269	c
46	12	Hybrid	T4	0.7735052	0.0086019	2080	0.7566360	0.7903745	a
48	9	Hybrid	T4	0.7717195	0.0086019	2080	0.7548503	0.7885888	a
47	3	Hybrid	T4	0.7083148	0.0086019	2080	0.6914455	0.7251840	b
49	6	Hybrid	T4	0.7058362	0.0086019	2080	0.6889669	0.7227055	b
50	0	Hybrid	T4	0.5836243	0.0086019	2080	0.5667550	0.6004936	c
56	12	Hybrid	T5	0.7615719	0.0086019	2080	0.7447026	0.7784412	a
58	9	Hybrid	T5	0.7597862	0.0086019	2080	0.7429169	0.7766555	a
57	3	Hybrid	T5	0.6963814	0.0086019	2080	0.6795122	0.7132507	b
59	6	Hybrid	T5	0.6939029	0.0086019	2080	0.6770336	0.7107721	b
60	0	Hybrid	T5	0.5716910	0.0086019	2080	0.5548217	0.5885602	c
66	12	Hybrid	T6	0.7741052	0.0086019	2080	0.7572360	0.7909745	a
68	9	Hybrid	T6	0.7723195	0.0086019	2080	0.7554503	0.7891888	a
67	3	Hybrid	T6	0.7089148	0.0086019	2080	0.6920455	0.7257840	b
69	6	Hybrid	T6	0.7064362	0.0086019	2080	0.6895669	0.7233055	b
70	0	Hybrid	T6	0.5842243	0.0086019	2080	0.5673550	0.6010936	c

p1a <- mc %>% 
  plot_smr(type = "line"
           , x = "tiempo"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Seed weight (g)"
           , xlab = "Time (h)"
           , glab = "Variety"
           , ylimits = c(0, 1, 0.2)
           ) + 
  facet_wrap(. ~ trat, ncol = 2)

p1a

6.1.2 Porcentaje de Germination

trait <- "pg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers %>% kable()

index	bloque	trat	variedad	pg	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: pg
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2   633.3   316.7  0.6222 0.544582   
## trat           6  7000.0  1166.7  2.2922 0.065673 . 
## variedad       1  4609.5  4609.5  9.0565 0.005753 **
## trat:variedad  6  6857.1  1142.9  2.2454 0.070466 . 
## Residuals     26 13233.3   509.0                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	variedad	trat	emmean	SE	df	lower.CL	upper.CL	group
2	Creole	T0	86.66667	13.02529	26	59.8928044	113.44053	a
1	Hybrid	T0	63.33333	13.02529	26	36.5594710	90.10720	a
3	Hybrid	T1	70.00000	13.02529	26	43.2261377	96.77386	a
4	Creole	T1	30.00000	13.02529	26	3.2261377	56.77386	b
5	Hybrid	T2	56.66667	13.02529	26	29.8928044	83.44053	a
6	Creole	T2	36.66667	13.02529	26	9.8928044	63.44053	a
7	Hybrid	T3	66.66667	13.02529	26	39.8928044	93.44053	a
8	Creole	T3	16.66667	13.02529	26	-10.1071956	43.44053	b
9	Hybrid	T4	76.66667	13.02529	26	49.8928044	103.44053	a
10	Creole	T4	26.66667	13.02529	26	-0.1071956	53.44053	b
11	Hybrid	T5	70.00000	13.02529	26	43.2261377	96.77386	a
12	Creole	T5	70.00000	13.02529	26	43.2261377	96.77386	a
13	Hybrid	T6	43.33333	13.02529	26	16.5594710	70.10720	a
14	Creole	T6	33.33333	13.02529	26	6.5594710	60.10720	a

p1b <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination ('%')"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 120, 20)
           ) 

p1b

6.1.3 Velocidad de germinación

trait <- "vg"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers %>% kable()

	index	bloque	trat	variedad	vg	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
7	7	1	T2	Creole	1	-1.666667	-3.372454	0.0007450	0.0007450159	0.0305456	OUTLIER
34	34	1	T4	Hybrid	5	1.888889	3.822114	0.0001323	0.0001323123	0.0055571	OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: vg
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2  1.3321 0.66607  1.3664 0.274150   
## trat           6  8.6594 1.44323  2.9608 0.026214 * 
## variedad       1  2.0003 2.00025  4.1035 0.054051 . 
## trat:variedad  6 11.5622 1.92703  3.9533 0.006872 **
## Residuals     24 11.6989 0.48745                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	variedad	trat	emmean	SE	df	lower.CL	upper.CL	group
2	Creole	T0	2.888889	0.4030931	24	2.0569455	3.720832	a
1	Hybrid	T0	2.333333	0.4030931	24	1.5013900	3.165277	a
3	Hybrid	T1	3.277778	0.4030931	24	2.4458344	4.109721	a
4	Creole	T1	1.500000	0.4030931	24	0.6680567	2.331943	b
6	Creole	T2	3.379630	0.5004960	24	2.3466566	4.412603	a
5	Hybrid	T2	2.833333	0.4030931	24	2.0013900	3.665277	a
7	Hybrid	T3	3.833333	0.4030931	24	3.0013900	4.665277	a
8	Creole	T3	1.666667	0.4030931	24	0.8347233	2.498610	b
9	Hybrid	T4	2.046296	0.5004960	24	1.0133232	3.079269	a
10	Creole	T4	1.333333	0.4030931	24	0.5013900	2.165277	a
12	Creole	T5	3.055556	0.4030931	24	2.2236122	3.887499	a
11	Hybrid	T5	3.000000	0.4030931	24	2.1680567	3.831943	a
14	Creole	T6	2.333333	0.4030931	24	1.5013900	3.165277	a
13	Hybrid	T6	1.833333	0.4030931	24	1.0013900	2.665277	a

p1c <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination speed (days)"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 6, 1)
           ) 

p1c

6.1.4 Indice de germinación

trait <- "ig"
fb <- germination

lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers %>% kable()

	index	bloque	trat	variedad	ig	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
25	25	1	T1	Hybrid	0.2	-1.466667	-3.29751	0.0009755	0.0009754607	0.0409693	OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: ig
##               Df  Sum Sq Mean Sq F value   Pr(>F)   
## bloque         2  0.3050  0.1525  0.4149 0.664896   
## trat           6 10.3540  1.7257  4.6949 0.002507 **
## variedad       1  3.8850  3.8850 10.5697 0.003278 **
## trat:variedad  6  6.5489  1.0915  2.9695 0.024965 * 
## Residuals     25  9.1890  0.3676                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	variedad	trat	emmean	SE	df	lower.CL	upper.CL	group
2	Creole	T0	2.6000000	0.3500293	25	1.8791012	3.3208988	a
1	Hybrid	T0	1.7666667	0.3500293	25	1.0457678	2.4875655	a
3	Hybrid	T1	2.3679487	0.4341579	25	1.4737838	3.2621137	a
4	Creole	T1	0.6000000	0.3500293	25	-0.1208988	1.3208988	b
5	Hybrid	T2	1.1333333	0.3500293	25	0.4124345	1.8542322	a
6	Creole	T2	0.5666667	0.3500293	25	-0.1542322	1.2875655	a
7	Hybrid	T3	1.2333333	0.3500293	25	0.5124345	1.9542322	a
8	Creole	T3	0.1666667	0.3500293	25	-0.5542322	0.8875655	b
9	Hybrid	T4	1.9666667	0.3500293	25	1.2457678	2.6875655	a
10	Creole	T4	0.5333333	0.3500293	25	-0.1875655	1.2542322	b
11	Hybrid	T5	1.7000000	0.3500293	25	0.9791012	2.4208988	a
12	Creole	T5	1.6666667	0.3500293	25	0.9457678	2.3875655	a
13	Hybrid	T6	1.0666667	0.3500293	25	0.3457678	1.7875655	a
14	Creole	T6	0.5333333	0.3500293	25	-0.1875655	1.2542322	a

p1d <- mc %>% 
  plot_smr(type = "bar"
           , x = "trat"
           , y = "emmean"
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , ylab = "Germination Index"
           , xlab = "Treatments"
           , glab = "Variety"
           , ylimits = c(0, 5, 1)
           ) 

p1d

6.2 Figura 1

legend <- cowplot::get_plot_component(p1b, 'guide-box-top', return_all = TRUE)

p1i <- list(p1b + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1c + labs(x = NULL) + theme(legend.position="none"
                                        , axis.title.x=element_blank()
                                        , axis.text.x=element_blank()
                                        , axis.ticks.x=element_blank())
           , p1d + labs(x = NULL) + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 1
            , labels = c("b", "c", "d")
            ) 

p1il <- list(legend, p1i) %>% 
  plot_grid(plotlist = ., ncol = 1, align = 'v', rel_heights = c(0.05, 1))


plot <- list(p1a, p1il) %>% 
  plot_grid(plotlist = .
            , ncol = 2
            , rel_widths = c(1.5, 1)
            , labels = c("a")
            , label_y = 0.96
            )  
  
plot %>% 
  ggsave2(plot = ., "files/Fig-1.jpg"
         , units = "cm"
         , width = 30
         , height = 25
         )

plot %>% 
  ggsave2(plot = ., "files/Fig-1.eps"
         , units = "cm"
         , width = 30
         , height = 25
         )

knitr::include_graphics("files/Fig-1.jpg")

6.3 Objetivo Específico 2

Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.

fb <- plantula

rsl <- 4:length(fb) %>% map(\(x) {
  
trait <- names(fb)[x]

cat("\n### ", trait)

lmm <- paste({{trait}}, "~ 1 + (1|trat) + trat*variedad") %>% as.formula()

lmd <- paste({{trait}}, "~ trat*variedad") %>% as.formula()

rmout <- fb %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

cat("\n#### ",  "Diagnostico")

rmout$diagplot %>% print()

cat("\n#### ", "Outliers")

rmout$outliers  %>% kable() %>% print()

cat("\n#### ", "ANOVA")

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model) %>% anova_table %>% kable() %>% print()

cat("\n#### ", "Mean comparison")

mc <- emmeans(model, ~ variedad|trat) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group") %>% 
  rename({{trait}} := "emmean")

mc %>% kable() %>% print()

plot <- mc %>% 
  plot_smr(x = "trat"
           , y = trait
           , group = "variedad"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Treatments"
           , glab = "Variety"
           )

plot

list(mc = mc, plot = plot)
  
})

6.3.1 raiz_lgtd

6.3.1.1 Diagnostico

6.3.1.2 Outliers

index	trat	variedad	raiz_lgtd	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.1.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	113.028571428572	18.8380952380953	2.20354897748071	0.0442610263913077	*
variedad	1	2.30476190476198	2.30476190476198	0.269594970955686	0.604189403305695	ns
trat:variedad	6	240.761904761907	40.1269841269844	4.69377470093635	0.000173605924663632	***
Residuals	196	1675.6	8.54897959183673
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.1.4 Mean comparison

	variedad	trat	raiz_lgtd	SE	df	lower.CL	upper.CL	group
1	Hybrid	T0	16.00000	0.7549384	196	14.511155	17.48885	a
2	Creole	T0	11.00000	0.7549384	196	9.511155	12.48885	b
4	Creole	T1	14.00000	0.7549384	196	12.511155	15.48885	a
3	Hybrid	T1	13.93333	0.7549384	196	12.444488	15.42218	a
5	Hybrid	T2	13.60000	0.7549384	196	12.111155	15.08885	a
6	Creole	T2	13.33333	0.7549384	196	11.844488	14.82218	a
8	Creole	T3	12.93333	0.7549384	196	11.444488	14.42218	a
7	Hybrid	T3	12.46667	0.7549384	196	10.977821	13.95551	a
10	Creole	T4	15.13333	0.7549384	196	13.644488	16.62218	a
9	Hybrid	T4	14.00000	0.7549384	196	12.511155	15.48885	a
12	Creole	T5	15.80000	0.7549384	196	14.311155	17.28885	a
11	Hybrid	T5	13.40000	0.7549384	196	11.911155	14.88885	b
13	Hybrid	T6	15.06667	0.7549384	196	13.577822	16.55551	a
14	Creole	T6	14.80000	0.7549384	196	13.311155	16.28885	a

6.3.2 gsr_raiz

6.3.2.1 Diagnostico

6.3.2.2 Outliers

index	trat	variedad	gsr_raiz	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.2.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	2.85575333333333	0.475958888888889	12.0927440494915	0.0000000000150374615277643	***
variedad	1	0.327257619047619	0.327257619047619	8.3146732160573	0.00437199311126879	**
trat:variedad	6	0.701705714285716	0.116950952380953	2.97138674474315	0.00845464188145274	**
Residuals	196	7.71437333333333	0.0393590476190476
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.2.4 Mean comparison

	variedad	trat	gsr_raiz	SE	df	lower.CL	upper.CL	group
2	Creole	T0	1.2560000	0.0512244	196	1.1549783	1.3570217	a
1	Hybrid	T0	1.0853333	0.0512244	196	0.9843116	1.1863550	b
3	Hybrid	T1	0.8926667	0.0512244	196	0.7916450	0.9936884	a
4	Creole	T1	0.7486667	0.0512244	196	0.6476450	0.8496884	b
5	Hybrid	T2	0.9500000	0.0512244	196	0.8489783	1.0510217	a
6	Creole	T2	0.9260000	0.0512244	196	0.8249783	1.0270217	a
8	Creole	T3	0.8600000	0.0512244	196	0.7589783	0.9610217	a
7	Hybrid	T3	0.7760000	0.0512244	196	0.6749783	0.8770217	a
10	Creole	T4	0.9846667	0.0512244	196	0.8836450	1.0856884	a
9	Hybrid	T4	0.7840000	0.0512244	196	0.6829783	0.8850217	b
12	Creole	T5	1.1066667	0.0512244	196	1.0056450	1.2076884	a
11	Hybrid	T5	0.9280000	0.0512244	196	0.8269783	1.0290217	b
14	Creole	T6	1.0513333	0.0512244	196	0.9503116	1.1523550	a
13	Hybrid	T6	0.9646667	0.0512244	196	0.8636450	1.0656884	a

6.3.3 num_raiz

6.3.3.1 Diagnostico

6.3.3.2 Outliers

index	trat	variedad	num_raiz	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.3.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	457.161904761906	76.193650793651	9.76160594968335	0.00000000208792569898294	***
variedad	1	4.28571428571413	4.28571428571413	0.549067456858964	0.45958580814979	ns
trat:variedad	6	153.714285714286	25.6190476190477	3.2822032421126	0.00424059446258091	**
Residuals	196	1529.86666666667	7.80544217687077
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.3.4 Mean comparison

	variedad	trat	num_raiz	SE	df	lower.CL	upper.CL	group
1	Hybrid	T0	11.33333	0.7213618	196	9.910706	12.75596	a
2	Creole	T0	10.86667	0.7213618	196	9.444039	12.28929	a
3	Hybrid	T1	15.73333	0.7213618	196	14.310706	17.15596	a
4	Creole	T1	14.53333	0.7213618	196	13.110706	15.95596	a
6	Creole	T2	15.06667	0.7213618	196	13.644039	16.48929	a
5	Hybrid	T2	12.00000	0.7213618	196	10.577373	13.42263	b
7	Hybrid	T3	12.53333	0.7213618	196	11.110706	13.95596	a
8	Creole	T3	10.66667	0.7213618	196	9.244039	12.08929	a
10	Creole	T4	13.40000	0.7213618	196	11.977373	14.82263	a
9	Hybrid	T4	10.93333	0.7213618	196	9.510706	12.35596	b
11	Hybrid	T5	11.80000	0.7213618	196	10.377373	13.22263	a
12	Creole	T5	11.33333	0.7213618	196	9.910706	12.75596	a
14	Creole	T6	10.73333	0.7213618	196	9.310706	12.15596	a
13	Hybrid	T6	10.26667	0.7213618	196	8.844039	11.68929	a

6.3.4 peso_fres_raiz

6.3.4.1 Diagnostico

6.3.4.2 Outliers

index	trat	variedad	peso_fres_raiz	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.4.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	73.5104057142858	12.2517342857143	6.56141211693855	0.00000248176481747279	***
variedad	1	0.00092190476190473	0.00092190476190473	0.000493725780723001	0.982295115363446	ns
trat:variedad	6	36.2561847619047	6.04269746031745	3.23616456335913	0.00469927715193408	**
Residuals	196	365.979133333333	1.86724047619048
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.4.4 Mean comparison

	variedad	trat	peso_fres_raiz	SE	df	lower.CL	upper.CL	group
2	Creole	T0	4.698000	0.3528211	196	4.002187	5.393813	a
1	Hybrid	T0	4.537333	0.3528211	196	3.841520	5.233146	a
4	Creole	T1	6.624000	0.3528211	196	5.928187	7.319813	a
3	Hybrid	T1	6.082667	0.3528211	196	5.386854	6.778480	a
6	Creole	T2	4.990000	0.3528211	196	4.294187	5.685813	a
5	Hybrid	T2	4.738667	0.3528211	196	4.042854	5.434480	a
7	Hybrid	T3	5.388000	0.3528211	196	4.692187	6.083813	a
8	Creole	T3	4.208000	0.3528211	196	3.512187	4.903813	b
10	Creole	T4	5.346000	0.3528211	196	4.650187	6.041813	a
9	Hybrid	T4	3.858667	0.3528211	196	3.162854	4.554480	b
11	Hybrid	T5	5.245333	0.3528211	196	4.549520	5.941146	a
12	Creole	T5	4.418000	0.3528211	196	3.722187	5.113813	a
13	Hybrid	T6	5.728667	0.3528211	196	5.032854	6.424480	a
14	Creole	T6	5.324667	0.3528211	196	4.628854	6.020480	a

6.3.5 peso_seco_raiz

6.3.5.1 Diagnostico

6.3.5.2 Outliers

	index	trat	variedad	peso_seco_raiz	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
124	124	T1	Hybrid	2.60	1.318667	4.904936	0.0000009	0.0000009345758	0.0001953	OUTLIER
153	153	T3	Hybrid	2.88	1.516000	5.638941	0.0000000	0.0000000171099	0.0000036	OUTLIER
193	193	T5	Hybrid	1.94	1.170000	4.351953	0.0000135	0.0000134930040	0.0028065	OUTLIER

6.3.5.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	8.89570540896218	1.48261756816036	14.9702912365665	0.0000000000000489299838764944	***
variedad	1	0.208061338505747	0.208061338505747	2.10083766669894	0.148841410422638	ns
trat:variedad	6	0.273239581034483	0.0455399301724139	0.459825940427679	0.837389504298845	ns
Residuals	193	19.1142033333333	0.099037322970639
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.5.4 Mean comparison

	variedad	trat	peso_seco_raiz	SE	df	lower.CL	upper.CL	group
1	Hybrid	T0	0.6646667	0.0812557	193	0.5044035	0.8249299	a
2	Creole	T0	0.6346667	0.0812557	193	0.4744035	0.7949299	a
4	Creole	T1	1.2140000	0.0812557	193	1.0537368	1.3742632	a
3	Hybrid	T1	1.1871429	0.0841076	193	1.0212547	1.3530310	a
5	Hybrid	T2	0.8340000	0.0812557	193	0.6737368	0.9942632	a
6	Creole	T2	0.8133333	0.0812557	193	0.6530701	0.9735965	a
7	Hybrid	T3	1.2557143	0.0841076	193	1.0898261	1.4216024	a
8	Creole	T3	1.0566667	0.0812557	193	0.8964035	1.2169299	a
9	Hybrid	T4	0.9240000	0.0812557	193	0.7637368	1.0842632	a
10	Creole	T4	0.8433333	0.0812557	193	0.6830701	1.0035965	a
11	Hybrid	T5	0.6864286	0.0841076	193	0.5205404	0.8523167	a
12	Creole	T5	0.5526667	0.0812557	193	0.3924035	0.7129299	a
13	Hybrid	T6	0.9426667	0.0812557	193	0.7824035	1.1029299	a
14	Creole	T6	0.9320000	0.0812557	193	0.7717368	1.0922632	a

6.3.6 alt_planta

6.3.6.1 Diagnostico

6.3.6.2 Outliers

index	trat	variedad	alt_planta	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.6.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	8203.78095238095	1367.29682539682	50.9551611949127	0.000000000000000000000000000000000000184040121052396	***
variedad	1	20.7428571428572	20.7428571428572	0.773025732031944	0.380359284820424	ns
trat:variedad	6	1204.52380952381	200.753968253969	7.48151434486839	0.000000313700914208267	***
Residuals	196	5259.33333333333	26.8333333333333
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.6.4 Mean comparison

	variedad	trat	alt_planta	SE	df	lower.CL	upper.CL	group
2	Creole	T0	30.80000	1.337493	196	28.16227	33.43773	a
1	Hybrid	T0	26.13333	1.337493	196	23.49561	28.77106	b
3	Hybrid	T1	46.33333	1.337493	196	43.69561	48.97106	a
4	Creole	T1	40.26667	1.337493	196	37.62894	42.90439	b
5	Hybrid	T2	44.20000	1.337493	196	41.56227	46.83773	a
6	Creole	T2	39.40000	1.337493	196	36.76227	42.03773	b
8	Creole	T3	38.26667	1.337493	196	35.62894	40.90439	a
7	Hybrid	T3	37.06667	1.337493	196	34.42894	39.70439	a
10	Creole	T4	40.53333	1.337493	196	37.89561	43.17106	a
9	Hybrid	T4	33.06667	1.337493	196	30.42894	35.70439	b
11	Hybrid	T5	30.26667	1.337493	196	27.62894	32.90439	a
12	Creole	T5	27.53333	1.337493	196	24.89561	30.17106	a
13	Hybrid	T6	28.80000	1.337493	196	26.16227	31.43773	a
14	Creole	T6	24.66667	1.337493	196	22.02894	27.30439	b

6.3.7 gsr_tallo

6.3.7.1 Diagnostico

6.3.7.2 Outliers

	index	trat	variedad	gsr_tallo	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
3	3	T0	Creole	6.59	2.118667	4.170295	0.0000304	0.00003042056	0.0063579	OUTLIER
139	139	T2	Hybrid	3.04	-2.168000	-4.267401	0.0000198	0.00001977638	0.0041530	OUTLIER

6.3.7.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	36.8710544473915	6.14517574123192	16.4230125647216	0.00000000000000285227440100774	***
variedad	1	1.40401584033855	1.40401584033855	3.7522392780789	0.0541891483916545	ns
trat:variedad	6	5.63568810054818	0.939281350091364	2.51023405414711	0.0231302956641592	*
Residuals	194	72.5910723809524	0.374180785468827
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.7.4 Mean comparison

	variedad	trat	gsr_tallo	SE	df	lower.CL	upper.CL	group
2	Creole	T0	4.320000	0.1634846	194	3.997565	4.642435	a
1	Hybrid	T0	3.890667	0.1579411	194	3.579165	4.202169	a
3	Hybrid	T1	4.917333	0.1579411	194	4.605831	5.228835	a
4	Creole	T1	4.849333	0.1579411	194	4.537831	5.160835	a
5	Hybrid	T2	5.362857	0.1634846	194	5.040422	5.685292	a
6	Creole	T2	5.032000	0.1579411	194	4.720498	5.343502	a
8	Creole	T3	4.509333	0.1579411	194	4.197831	4.820835	a
7	Hybrid	T3	4.016000	0.1579411	194	3.704498	4.327502	b
10	Creole	T4	4.316667	0.1579411	194	4.005165	4.628169	a
9	Hybrid	T4	3.704000	0.1579411	194	3.392498	4.015502	b
12	Creole	T5	4.195333	0.1579411	194	3.883831	4.506835	a
11	Hybrid	T5	4.066667	0.1579411	194	3.755165	4.378169	a
13	Hybrid	T6	4.218000	0.1579411	194	3.906498	4.529502	a
14	Creole	T6	4.095333	0.1579411	194	3.783831	4.406835	a

6.3.8 nhp_hoja

6.3.8.1 Diagnostico

6.3.8.2 Outliers

	index	trat	variedad	nhp_hoja	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
4	4	T0	Creole	6	1.0000000	10.117361	0.0000000	0.0000000000000000000	0.0000000	OUTLIER
5	5	T0	Creole	4	-1.0000000	-10.117361	0.0000000	0.0000000000000000000	0.0000000	OUTLIER
16	16	T1	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
17	17	T1	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
18	18	T1	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
19	19	T1	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
20	20	T1	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
21	21	T1	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
22	22	T1	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
23	23	T1	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
24	24	T1	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
25	25	T1	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
26	26	T1	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
27	27	T1	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
28	28	T1	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
29	29	T1	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
30	30	T1	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
31	31	T2	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
32	32	T2	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
33	33	T2	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
34	34	T2	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
35	35	T2	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
36	36	T2	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
37	37	T2	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
38	38	T2	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
39	39	T2	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
40	40	T2	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
41	41	T2	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
42	42	T2	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
43	43	T2	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
44	44	T2	Creole	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
45	45	T2	Creole	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
51	51	T3	Creole	6	0.8666667	8.768380	0.0000000	0.0000000000000000000	0.0000000	OUTLIER
57	57	T3	Creole	6	0.8666667	8.768380	0.0000000	0.0000000000000000000	0.0000000	OUTLIER
62	62	T4	Creole	6	0.7333333	7.419398	0.0000000	0.0000000000001176836	0.0000000	OUTLIER
65	65	T4	Creole	6	0.7333333	7.419398	0.0000000	0.0000000000001176836	0.0000000	OUTLIER
66	66	T4	Creole	6	0.7333333	7.419398	0.0000000	0.0000000000001176836	0.0000000	OUTLIER
72	72	T4	Creole	6	0.7333333	7.419398	0.0000000	0.0000000000001176836	0.0000000	OUTLIER
88	88	T5	Creole	6	0.9333333	9.442871	0.0000000	0.0000000000000000000	0.0000000	OUTLIER
112	112	T0	Hybrid	4	-0.6666667	-6.744908	0.0000000	0.0000000000153124180	0.0000000	OUTLIER
114	114	T0	Hybrid	4	-0.6666667	-6.744908	0.0000000	0.0000000000153124180	0.0000000	OUTLIER
116	116	T0	Hybrid	4	-0.6666667	-6.744908	0.0000000	0.0000000000153124180	0.0000000	OUTLIER
119	119	T0	Hybrid	4	-0.6666667	-6.744908	0.0000000	0.0000000000153124180	0.0000000	OUTLIER
120	120	T0	Hybrid	4	-0.6666667	-6.744908	0.0000000	0.0000000000153124180	0.0000000	OUTLIER
121	121	T1	Hybrid	6	0.5333333	5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
122	122	T1	Hybrid	6	0.5333333	5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
123	123	T1	Hybrid	5	-0.4666667	-4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
124	124	T1	Hybrid	6	0.5333333	5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
125	125	T1	Hybrid	6	0.5333333	5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
126	126	T1	Hybrid	6	0.5333333	5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
127	127	T1	Hybrid	6	0.5333333	5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
128	128	T1	Hybrid	5	-0.4666667	-4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
129	129	T1	Hybrid	5	-0.4666667	-4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
130	130	T1	Hybrid	5	-0.4666667	-4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
131	131	T1	Hybrid	5	-0.4666667	-4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
132	132	T1	Hybrid	6	0.5333333	5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
133	133	T1	Hybrid	5	-0.4666667	-4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
134	134	T1	Hybrid	5	-0.4666667	-4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
135	135	T1	Hybrid	5	-0.4666667	-4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
136	136	T2	Hybrid	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
137	137	T2	Hybrid	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
138	138	T2	Hybrid	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
139	139	T2	Hybrid	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
140	140	T2	Hybrid	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
141	141	T2	Hybrid	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
142	142	T2	Hybrid	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
143	143	T2	Hybrid	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
144	144	T2	Hybrid	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
145	145	T2	Hybrid	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
146	146	T2	Hybrid	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
147	147	T2	Hybrid	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
148	148	T2	Hybrid	6	0.4666667	4.721435	0.0000023	0.0000023418611010406	0.0003817	OUTLIER
149	149	T2	Hybrid	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
150	150	T2	Hybrid	5	-0.5333333	-5.395926	0.0000001	0.0000000681710119466	0.0000130	OUTLIER
152	152	T3	Hybrid	6	0.8666667	8.768380	0.0000000	0.0000000000000000000	0.0000000	OUTLIER
155	155	T3	Hybrid	6	0.8666667	8.768380	0.0000000	0.0000000000000000000	0.0000000	OUTLIER
166	166	T4	Hybrid	6	0.8666667	8.768380	0.0000000	0.0000000000000000000	0.0000000	OUTLIER
173	173	T4	Hybrid	6	0.8666667	8.768380	0.0000000	0.0000000000000000000	0.0000000	OUTLIER
181	181	T5	Hybrid	6	0.9333333	9.442871	0.0000000	0.0000000000000000000	0.0000000	OUTLIER

6.3.8.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	4	0.000000000000000000000000000448210187366446	0.000000000000000000000000000112052546841611	1.17465980750084	0.325411307782507	ns
variedad	1	0.0000000000000000000000000000724842873482364	0.0000000000000000000000000000724842873482364	0.759861167133205	0.385098313680927	ns
trat:variedad	4	0.000000000000000000000000000345717525650355	0.0000000000000000000000000000864293814125887	0.906049200970291	0.46279233587861	ns
Residuals	121	0.0000000000000000000000000115423700387614	0.0000000000000000000000000000953914879236483
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.8.4 Mean comparison

	variedad	trat	nhp_hoja	SE	df	lower.CL	upper.CL	group
2	Creole	T0	5	0	121	5	5	a
1	Hybrid	T0	5	0	121	5	5	b
3	Hybrid	T3	5	0	121	5	5	a
4	Creole	T3	5	0	121	5	5	a
5	Hybrid	T4	5	0	121	5	5	a
6	Creole	T4	5	0	121	5	5	a
7	Hybrid	T5	5	0	121	5	5	a
8	Creole	T5	5	0	121	5	5	a
9	Hybrid	T6	5	0	121	5	5	a
10	Creole	T6	5	0	121	5	5	a

6.3.9 larg_hoja

6.3.9.1 Diagnostico

6.3.9.2 Outliers

index	trat	variedad	larg_hoja	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.9.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	3517.25714285714	586.209523809524	44.1413789570741	0.000000000000000000000000000000000679814077816785	***
variedad	1	12.8761904761905	12.8761904761905	0.96957278967319	0.326000686912924	ns
trat:variedad	6	830.857142857144	138.476190476191	10.4272103268108	0.000000000500170136061446	***
Residuals	196	2602.93333333333	13.2802721088435
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.9.4 Mean comparison

	variedad	trat	larg_hoja	SE	df	lower.CL	upper.CL	group
2	Creole	T0	26.20000	0.9409312	196	24.34435	28.05565	a
1	Hybrid	T0	22.66667	0.9409312	196	20.81102	24.52232	b
3	Hybrid	T1	36.26667	0.9409312	196	34.41102	38.12232	a
4	Creole	T1	28.33333	0.9409312	196	26.47768	30.18898	b
6	Creole	T2	34.80000	0.9409312	196	32.94435	36.65565	a
5	Hybrid	T2	33.86667	0.9409312	196	32.01102	35.72232	a
7	Hybrid	T3	28.93333	0.9409312	196	27.07768	30.78898	a
8	Creole	T3	28.60000	0.9409312	196	26.74435	30.45565	a
10	Creole	T4	28.26667	0.9409312	196	26.41102	30.12232	a
9	Hybrid	T4	23.33333	0.9409312	196	21.47768	25.18898	b
11	Hybrid	T5	26.46667	0.9409312	196	24.61102	28.32232	a
12	Creole	T5	23.40000	0.9409312	196	21.54435	25.25565	b
13	Hybrid	T6	23.06667	0.9409312	196	21.21102	24.92232	a
14	Creole	T6	21.53333	0.9409312	196	19.67768	23.38898	a

6.3.10 grs_hoja

6.3.10.1 Diagnostico

6.3.10.2 Outliers

	index	trat	variedad	grs_hoja	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
193	193	T5	Hybrid	1.43	0.5793333	3.757259	0.0001718	0.0001717844	0.0359029	OUTLIER
197	197	T6	Hybrid	1.45	0.6780000	4.397161	0.0000110	0.0000109676	0.0023032	OUTLIER

6.3.10.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	1.87838186229001	0.313063643715002	10.961029990429	0.000000000166359834805947	***
variedad	1	0.000634076170869784	0.000634076170869784	0.022200367448119	0.881710078225411	ns
trat:variedad	6	0.632610833517145	0.105435138919524	3.69151047380647	0.00169800034430951	**
Residuals	194	5.54093428571428	0.0285615169366716
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.10.4 Mean comparison

	variedad	trat	grs_hoja	SE	df	lower.CL	upper.CL	group
2	Creole	T0	0.9700000	0.0436360	194	0.8839381	1.0560619	a
1	Hybrid	T0	0.8846667	0.0436360	194	0.7986048	0.9707285	a
3	Hybrid	T1	0.8446667	0.0436360	194	0.7586048	0.9307285	a
4	Creole	T1	0.6346667	0.0436360	194	0.5486048	0.7207285	b
5	Hybrid	T2	0.7553333	0.0436360	194	0.6692715	0.8413952	a
6	Creole	T2	0.7006667	0.0436360	194	0.6146048	0.7867285	a
7	Hybrid	T3	0.6880000	0.0436360	194	0.6019381	0.7740619	a
8	Creole	T3	0.6300000	0.0436360	194	0.5439381	0.7160619	a
10	Creole	T4	0.6853333	0.0436360	194	0.5992715	0.7713952	a
9	Hybrid	T4	0.6366667	0.0436360	194	0.5506048	0.7227285	a
12	Creole	T5	0.9406667	0.0436360	194	0.8546048	1.0267285	a
11	Hybrid	T5	0.8092857	0.0451676	194	0.7202032	0.8983682	b
14	Creole	T6	0.8126667	0.0436360	194	0.7266048	0.8987285	a
13	Hybrid	T6	0.7235714	0.0451676	194	0.6344889	0.8126539	a

6.3.11 anch_hoja

6.3.11.1 Diagnostico

6.3.11.2 Outliers

	index	trat	variedad	anch_hoja	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
141	141	T2	Hybrid	30.38	8.610667	3.680491	0.0002328	0.0002327851	0.0488849	OUTLIER

6.3.11.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	251.760531761535	41.9600886269226	6.40463281705183	0.00000355932128610583	***
variedad	1	0.0145229907818354	0.0145229907818354	0.00221673562680236	0.962495880334166	ns
trat:variedad	6	116.659083347477	19.4431805579129	2.96773520609711	0.00853268555104408	**
Residuals	195	1277.54666285714	6.55152134798535
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.11.4 Mean comparison

	variedad	trat	anch_hoja	SE	df	lower.CL	upper.CL	group
2	Creole	T0	18.86800	0.6608843	195	17.56460	20.17140	a
1	Hybrid	T0	16.61133	0.6608843	195	15.30793	17.91473	b
3	Hybrid	T1	20.04333	0.6608843	195	18.73993	21.34673	a
4	Creole	T1	18.95867	0.6608843	195	17.65527	20.26207	a
5	Hybrid	T2	21.15429	0.6840803	195	19.80514	22.50343	a
6	Creole	T2	19.03467	0.6608843	195	17.73127	20.33807	b
8	Creole	T3	19.05067	0.6608843	195	17.74727	20.35407	a
7	Hybrid	T3	17.51800	0.6608843	195	16.21460	18.82140	a
10	Creole	T4	17.06933	0.6608843	195	15.76593	18.37273	a
9	Hybrid	T4	16.07400	0.6608843	195	14.77060	17.37740	a
11	Hybrid	T5	19.51800	0.6608843	195	18.21460	20.82140	a
12	Creole	T5	18.28667	0.6608843	195	16.98327	19.59007	a
13	Hybrid	T6	17.86067	0.6608843	195	16.55727	19.16407	a
14	Creole	T6	17.55467	0.6608843	195	16.25127	18.85807	a

6.3.12 peso_fres_brote

6.3.12.1 Diagnostico

6.3.12.2 Outliers

index	trat	variedad	peso_fres_brote	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag

6.3.12.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	225.698459161905	37.6164098603175	28.7634400302011	0.00000000000000000000000146687997518286	***
variedad	1	0.375666304761905	0.375666304761905	0.287253761550096	0.59259348215828	ns
trat:variedad	6	67.1975631619048	11.1995938603175	8.56378499596482	0.0000000284330749762502	***
Residuals	196	256.3259584	1.30778550204082
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.12.4 Mean comparison

	variedad	trat	peso_fres_brote	SE	df	lower.CL	upper.CL	group
2	Creole	T0	5.392000	0.2952722	196	4.809681	5.974319	a
1	Hybrid	T0	3.613333	0.2952722	196	3.031015	4.195652	b
3	Hybrid	T1	7.640000	0.2952722	196	7.057681	8.222319	a
4	Creole	T1	5.770000	0.2952722	196	5.187681	6.352319	b
5	Hybrid	T2	7.163333	0.2952722	196	6.581015	7.745652	a
6	Creole	T2	6.562000	0.2952722	196	5.979681	7.144319	a
8	Creole	T3	5.699333	0.2952722	196	5.117015	6.281652	a
7	Hybrid	T3	4.771333	0.2952722	196	4.189015	5.353652	b
10	Creole	T4	5.014000	0.2952722	196	4.431681	5.596319	a
9	Hybrid	T4	4.094000	0.2952722	196	3.511681	4.676319	b
11	Hybrid	T5	4.720000	0.2952722	196	4.137681	5.302319	a
12	Creole	T5	4.684800	0.2952722	196	4.102481	5.267119	a
13	Hybrid	T6	4.303333	0.2952722	196	3.721015	4.885652	a
14	Creole	T6	3.775333	0.2952722	196	3.193015	4.357652	a

6.3.13 peso_seco_brote

6.3.13.1 Diagnostico

6.3.13.2 Outliers

	index	trat	variedad	peso_seco_brote	resi	res_MAD	rawp.BHStud	adjp	bholm	out_flag
42	42	T2	Creole	2.39	1.208667	3.733899	0.0001885	0.00018853852782	0.0384619	OUTLIER
72	72	T4	Creole	3.09	1.768000	5.461830	0.0000000	0.00000004712499	0.0000099	OUTLIER
127	127	T1	Hybrid	3.27	1.246667	3.851291	0.0001175	0.00011749690142	0.0240869	OUTLIER
134	134	T1	Hybrid	0.73	-1.293333	-3.995457	0.0000646	0.00006456965482	0.0133013	OUTLIER
167	167	T4	Hybrid	0.27	-1.193333	-3.686530	0.0002273	0.00022733296863	0.0461486	OUTLIER
169	169	T4	Hybrid	2.85	1.386667	4.283789	0.0000184	0.00001837374658	0.0038034	OUTLIER
170	170	T4	Hybrid	2.93	1.466667	4.530930	0.0000059	0.00000587245145	0.0012215	OUTLIER
172	172	T4	Hybrid	2.98	1.516667	4.685394	0.0000028	0.00000279422122	0.0005840	OUTLIER

6.3.13.3 ANOVA

Factor	Df	Sum Sq	Mean Sq	F value	Pr(>F)	Sig
trat	6	43.1935007107252	7.19891678512086	42.4711267015912	0.000000000000000000000000000000016240034181697	***
variedad	1	0.0152306451812492	0.0152306451812492	0.0898555547380109	0.764692030144416	ns
trat:variedad	6	3.37242174716203	0.562070291193672	3.31602090495423	0.00398028439930072	**
Residuals	188	31.8662691741592	0.169501431777442
—
Significance:		0.001 ***	0.01 **	0.05 *

6.3.13.4 Mean comparison

	variedad	trat	peso_seco_brote	SE	df	lower.CL	upper.CL	group
2	Creole	T0	0.7100000	0.1063019	188	0.5003022	0.9196978	a
1	Hybrid	T0	0.4253333	0.1063019	188	0.2156355	0.6350311	a
3	Hybrid	T1	2.0269231	0.1141866	188	1.8016715	2.2521747	a
4	Creole	T1	1.5046667	0.1063019	188	1.2949689	1.7143645	b
5	Hybrid	T2	1.2113333	0.1063019	188	1.0016355	1.4210311	a
6	Creole	T2	1.0950000	0.1100329	188	0.8779421	1.3120579	a
8	Creole	T3	1.7186667	0.1063019	188	1.5089689	1.9283645	a
7	Hybrid	T3	1.4093333	0.1063019	188	1.1996355	1.6190311	b
10	Creole	T4	1.1957143	0.1100329	188	0.9786564	1.4127721	a
9	Hybrid	T4	1.1745455	0.1241339	188	0.9296712	1.4194197	a
12	Creole	T5	0.6500000	0.1063019	188	0.4403022	0.8596978	a
11	Hybrid	T5	0.6373333	0.1063019	188	0.4276355	0.8470311	a
14	Creole	T6	0.5486667	0.1063019	188	0.3389689	0.7583645	a
13	Hybrid	T6	0.4586667	0.1063019	188	0.2489689	0.6683645	a

6.3.14 Figure 2

fig <- 1:length(rsl) %>% map(\(x) { rsl[[x]]$plot }) %>% 
    plot_grid(plotlist = .
              , ncol = 3
              , labels = "auto"
              ) 

fig %>% 
  ggsave2(plot = .
          , "files/Fig-2.jpg"
          , units = "cm"
          , width = 30
          , height = 40
         )

include_graphics("files/Fig-2.jpg")

6.3.15 PCA

blues <- 1:length(rsl) %>% map(\(x) { 
  
  rsl[[x]]$mc %>% 
    select(1:3)
  
  }) %>% 
  Reduce(function(...) merge(..., all = TRUE), .) 

blues %>% str()
## 'data.frame':    14 obs. of  15 variables:
##  $ variedad       : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 2 2 2 ...
##  $ trat           : Factor w/ 7 levels "T0","T1","T2",..: 1 2 3 4 5 6 7 1 2 3 ...
##  $ raiz_lgtd      : num  11 14 13.3 12.9 15.1 ...
##  $ gsr_raiz       : num  1.256 0.749 0.926 0.86 0.985 ...
##  $ num_raiz       : num  10.9 14.5 15.1 10.7 13.4 ...
##  $ peso_fres_raiz : num  4.7 6.62 4.99 4.21 5.35 ...
##  $ peso_seco_raiz : num  0.635 1.214 0.813 1.057 0.843 ...
##  $ alt_planta     : num  30.8 40.3 39.4 38.3 40.5 ...
##  $ gsr_tallo      : num  4.32 4.85 5.03 4.51 4.32 ...
##  $ nhp_hoja       : num  5 NA NA 5 5 ...
##  $ larg_hoja      : num  26.2 28.3 34.8 28.6 28.3 ...
##  $ grs_hoja       : num  0.97 0.635 0.701 0.63 0.685 ...
##  $ anch_hoja      : num  18.9 19 19 19.1 17.1 ...
##  $ peso_fres_brote: num  5.39 5.77 6.56 5.7 5.01 ...
##  $ peso_seco_brote: num  0.71 1.5 1.09 1.72 1.2 ...

pca <- blues %>% 
  select(!c(nhp_hoja)) %>% 
  unite("treat", c(trat, variedad), remove = F, sep = "-") %>% 
  column_to_rownames("treat") %>% 
  PCA(scale.unit = T, quali.sup = c(1:2), graph = F)

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = c(1:2), graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4  Dim.5  Dim.6  Dim.7  Dim.8
## Variance               6.27   2.46   1.22   0.79   0.55   0.32   0.20   0.09
## % of var.             52.22  20.54  10.20   6.57   4.59   2.65   1.64   0.72
## Cumulative % of var.  52.22  72.76  82.96  89.54  94.12  96.77  98.41  99.13
##                       Dim.9 Dim.10 Dim.11 Dim.12
## Variance               0.06   0.03   0.01   0.00
## % of var.              0.53   0.23   0.11   0.01
## Cumulative % of var.  99.66  99.89  99.99 100.00
## 
## Individuals
##                    Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## T0-Creole       |  4.15 | -1.64  3.07  0.16 |  2.99 25.86  0.52 | -1.37 10.97
## T1-Creole       |  3.89 |  3.03 10.44  0.61 | -1.65  7.88  0.18 |  1.40 11.38
## T2-Creole       |  3.15 |  2.46  6.89  0.61 |  1.00  2.88  0.10 |  0.02  0.00
## T3-Creole       |  2.86 |  1.26  1.82  0.20 | -0.85  2.08  0.09 | -2.16 27.31
## T4-Creole       |  1.99 |  0.26  0.08  0.02 | -0.77  1.73  0.15 |  0.82  3.89
## T5-Creole       |  3.56 | -2.85  9.28  0.64 |  1.59  7.31  0.20 |  0.67  2.58
## T6-Creole       |  3.08 | -2.74  8.55  0.79 | -0.43  0.53  0.02 |  0.81  3.80
## T0-Hybrid       |  4.00 | -3.75 16.03  0.88 |  0.17  0.08  0.00 |  0.92  4.95
## T1-Hybrid       |  5.13 |  4.70 25.13  0.84 |  0.81  1.90  0.02 |  1.15  7.69
## T2-Hybrid       |  3.97 |  2.81  8.98  0.50 |  2.13 13.09  0.29 | -0.76  3.40
## T3-Hybrid       |  2.92 |  1.07  1.30  0.13 | -2.18 13.77  0.56 | -0.73  3.10
## T4-Hybrid       |  3.68 | -1.68  3.22  0.21 | -2.59 19.46  0.50 | -1.64 15.76
## T5-Hybrid       |  2.02 | -1.06  1.28  0.27 |  0.66  1.26  0.11 | -0.05  0.01
## T6-Hybrid       |  2.69 | -1.85  3.92  0.47 | -0.86  2.17  0.10 |  0.94  5.16
##                  cos2  
## T0-Creole        0.11 |
## T1-Creole        0.13 |
## T2-Creole        0.00 |
## T3-Creole        0.57 |
## T4-Creole        0.17 |
## T5-Creole        0.03 |
## T6-Creole        0.07 |
## T0-Hybrid        0.05 |
## T1-Hybrid        0.05 |
## T2-Hybrid        0.04 |
## T3-Hybrid        0.06 |
## T4-Hybrid        0.20 |
## T5-Hybrid        0.00 |
## T6-Hybrid        0.12 |
## 
## Variables
##                   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr  cos2  
## raiz_lgtd       | -0.37  2.21  0.14 | -0.20  1.57  0.04 |  0.66 36.02  0.44 |
## gsr_raiz        | -0.56  5.07  0.32 |  0.75 22.69  0.56 |  0.09  0.60  0.01 |
## num_raiz        |  0.78  9.77  0.61 |  0.02  0.02  0.00 |  0.42 14.44  0.18 |
## peso_fres_raiz  |  0.48  3.61  0.23 | -0.15  0.90  0.02 |  0.70 39.99  0.49 |
## peso_seco_raiz  |  0.64  6.50  0.41 | -0.64 16.61  0.41 |  0.04  0.12  0.00 |
## alt_planta      |  0.95 14.40  0.90 | -0.01  0.00  0.00 | -0.08  0.58  0.01 |
## gsr_tallo       |  0.81 10.40  0.65 |  0.43  7.56  0.19 |  0.10  0.84  0.01 |
## larg_hoja       |  0.92 13.57  0.85 |  0.27  2.99  0.07 | -0.05  0.23  0.00 |
## grs_hoja        | -0.43  2.96  0.19 |  0.76 23.41  0.58 |  0.21  3.53  0.04 |
## anch_hoja       |  0.65  6.77  0.42 |  0.61 14.87  0.37 | -0.05  0.21  0.00 |
## peso_fres_brote |  0.90 12.88  0.81 |  0.41  6.82  0.17 | -0.06  0.25  0.00 |
## peso_seco_brote |  0.86 11.86  0.74 | -0.25  2.56  0.06 | -0.20  3.20  0.04 |
## 
## Supplementary categories
##                    Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3  cos2
## Creole          |  0.40 | -0.03  0.01  -0.05 |  0.27  0.45   0.62 |  0.02  0.00
## Hybrid          |  0.40 |  0.03  0.01   0.05 | -0.27  0.45  -0.62 | -0.02  0.00
## T0              |  3.24 | -2.70  0.69  -1.59 |  1.58  0.24   1.48 | -0.23  0.00
## T1              |  4.15 |  3.86  0.87   2.27 | -0.42  0.01  -0.39 |  1.27  0.09
## T2              |  3.31 |  2.63  0.63   1.55 |  1.56  0.22   1.46 | -0.37  0.01
## T3              |  2.46 |  1.16  0.22   0.68 | -1.51  0.38  -1.42 | -1.45  0.34
## T4              |  2.24 | -0.71  0.10  -0.42 | -1.68  0.57  -1.58 | -0.41  0.03
## T5              |  2.43 | -1.96  0.65  -1.15 |  1.12  0.21   1.05 |  0.31  0.02
## T6              |  2.79 | -2.30  0.68  -1.35 | -0.65  0.05  -0.61 |  0.87  0.10
##                 v.test  
## Creole            0.08 |
## Hybrid           -0.08 |
## T0               -0.30 |
## T1                1.69 |
## T2               -0.49 |
## T3               -1.92 |
## T4               -0.55 |
## T5                0.41 |
## T6                1.16 |

6.3.16 Figure 3

var <- pca %>% 
  plot.PCA(choix = "var"
           , cex=0.8 
           )

ind <- pca %>% 
  plot.PCA(choix = "ind", habillage = 2
           , label = c("ind")
           , invisible = "quali"
           ) +
  labs(colour = "Treatments") +
  theme(legend.position = "bottom"
        , legend.direction = "horizontal")
  

fig <- list(var, ind) %>% 
    plot_grid(plotlist = .
              , ncol = 2
              , labels = "auto"
              , rel_widths = c(1.5, 2)
              ) 

fig %>% 
  ggsave2(plot = .
          , "files/Fig-3.jpg"
          , units = "cm"
          , width = 30
          , height = 12
         )

fig %>% 
  ggsave2(plot = .
          , "files/Fig-3.eps"
          , units = "cm"
          , width = 30
          , height = 12
         )

include_graphics("files/Fig-3.jpg")